Our formulation of the generalization gap is well-suited to neural networks that accept bounded inputs (i.e., pixels with real values between 0 and 255), where the inputs are normalized before entry to the model, and that use batch normalization [1]} and ReLU activation functions, which further standardize and constrain the model's internal representations.
